There are various conventional scanning systems that can be employed to acquire the geometry of an object. Typically, a system acquires the entire geometry, without separating out the large scale shape of the object and the small scale surface variations. However, the small scale surface variations, such as small bumps and ridges, form a geometric texture that alters the appearance of the large scale shape. For example, small bumps on an orange peel differentiate the appearance of an orange from that of a smooth ball.
In computer graphics such small scale surface variations can be represented as "bump maps". Bump maps are used to store a complex geometry at a physical scale between explicit geometry representation and surface microscale effects as encapsulated in a bidirectional reflectance distribution function (BRDF). A bump map contains an array of normals that are applied as perturbations to a base geometry, and are most frequently defined procedurally.
Two conventional methods for capturing bump maps for curved objects have been developed. One method is to flatten the object, X-ray it, generate a height map from the X-ray, and then compute surface normals from the height map. The other method uses 3-d scanning techniques from computer vision methodologies to acquire a detailed triangle mesh representing the object, and then uses surface normals computed from the mesh to represent the bump map. In this case the bump map only exists for the curved object, and the normals only apply to objects with the original curvature.
In computer vision, surface normals are obtained directly from objects using variations of photometric stereo. However, these normals include the effect of the large scale geometry of the object, as well as the small scale surface variations.
Prior art systems for acquiring bump maps either require altering the original object, such as by flattening the object, or producing maps that are specific to the original large scale geometry. These techniques cannot be applied for objects that cannot be altered, such as existing artwork.
Also, prior art systems involving X-ray or 3-d scanning systems for detailed geometry acquisition are expensive and require a trained operator to use. In the case of 3-d scanning systems complex software is needed to form the detailed 3-d surface, and subsequently compute the surface normals.
It is further noted that for computer vision techniques it is known to obtain a "shape from" various lighting effects, such as shape from shading and shape from specularity. Researchers have also explored the area of "appearance based matching." In particular, various researchers have sought illumination basis images that can be used to construct an image of an object under arbitrary lighting conditions.
Epstein et al. in "Learning Object Representations from Lighting Variations", ECCV 96 International Workshop, pgs. 179-199 (April 1996) us ed the idea of illumination basis images to develop methods for obtaining an object's shape from lighting variations. These authors addressed the general problem of obtaining an object's geometry given a series of images of the object under unknown illumination conditions.
Other researchers have addressed the problem of obtaining input for rendering using inexpensive equipment. One approach measures anisotropic reflectance using a CCD camera, a quartz-halogen lamp, and a diffuse reference standard. Another approach re-renders the appearance of an object for fixed lighting conditions from a large series of video images, while another approach obtains texture maps from non-planar shiny objects using a series of video images.